Awareness Analysis for a Better Brand

When most people hear “brand awareness,” they think one of two things:

That’s a waste of time and money; it doesn’t work

That’s only for big-name brands like Coca-Cola and Tesla

While brand awareness often sounds elusive, intangible, or for major corporations only … it’s not.

In fact, brand awareness can yield tangible results for businesses of all sizes. Just take a moment to think about a local mom-and-pop shop or restaurant in your town. Where’s the first place you turn locally for something like a slice of pizza?

Odds are a specific place immediately jumped to mind. Why? Brand awareness. It might not be the result of an ad campaign, but that brand is positioned — in your eyes — as the number one choice.

And data backs this theory up.

In a Search Engine Land and SurveyMonkey report, users were asked: “What is most important in helping you decide which results to click on in a search engine search?” The responses showed that 70% of consumers rank “Known retailer” as the most determinative factor.

Simply put, the vast majority of consumers have severe brand bias. More than ratings (social proof), free shipping, free returns, or discounts and sales, people are clicking on brands they recognize.

KPIs like impressions and click-through rate (CTR) can help you gauge how many new users you’re reaching and developing awareness with. CTR and new users tell you how well your brand name stands out in search results or how effective different advertising strategies are.

Using the custom Google Data Studio reports built for Shopify Plus merchants, we can analyze data based solely on new users:

With metrics like bounce rate, average session duration, and pages per session, you can see how engaged new, unaware visitors are. You can also measure the effectiveness of various channels:

Of course, the danger of a metric like brand awareness is getting lost in vanity metrics that don’t tell the whole story. That’s why it’s critical to look closely at your spend and most importantly, customer acquisition costs:

Creating thousands — or even millions — of impressions has to be balanced with acquisition costs both among new users (as illustrated above) as well as over time.

To get a better understanding of how each channel plays a role in brand awareness, let’s conduct a channel analysis.

Regarding KPIs for a channel analysis, we again want to focus on the very same metrics:

Conducting a channel analysis involves putting these metrics to the test with each channel that you use for brand-building, and — in this case — looking only at first-time visitor data. For example, running cold Facebook Ad campaigns and inspecting their results is a powerful overview of your awareness generation performance:

Here, we can see everything from impressions to CTR to CPM. If your data is connected in Google Analytics, you can further assess how this channel played a role in developing awareness on site.

In Google Analytics, inspect your social network data with a secondary dimension of “Landing Page” and the segment of “new users” to see how unaware users interacted on each page they landed on.

Using multiple variables like a secondary dimension can help you avoid a static analysis and elicit true performance.

To get even more specific, add “Event Action” as your secondary dimension to inspect conversions — like email signups and form completion — from this channel.

In this example, we found that users who moved from unaware to engaged (e.g., signed up for the email list), often browsed nearly seven pages per session. Utilizing this data, you can head over to the “Behavior Flow” section of Analytics and run a report keeping “New Users” as your segment, and Facebook as your traffic source:

As earlier data showed, it took over six page visits on average before a conversion action. Now you can see exactly what those common pages were, giving you a clear understanding of the conversion path and how to better optimize user flow.

This user flow reveals the “why” behind conversions. Why they had to go to six or more pages, what those pages communicated, and how they nudged users down the funnel.

Such flows are common when moving users from unaware to aware to engaged. If you landed on Gymshark for the first time, ecommerce-wide data tells us you wouldn’t be ready to buy. Instead, you’d be looking to learn more about the company and its products.

Your first touchpoint (after the home or landing page) might be an “About” section:

Once educated, you might investigate where they sell their products in-store:

Or maybe even micro-conversion on their latest products by taking a look different images and seeing what sizes are in stock:

When doing this type of data analysis for the top of your funnel, pay special attention to micro-conversions.

Micro-conversions are the small, seemly insignificant steps taken by a user that leads to engagement and eventually acquisition. Shopping on an UNTUCKit product page, this would include a new visitor clicking:

Images

Reviews

Colors

Size Chart

Size or Fit Selector

Shipping

Returns & Exchanges

Essentially, these are signals that show you how the buying process works and what features are most important for consumers before they commit.

To track all those micro conversions, configure the various clicks as Events in Google Analytics on your most popular pages — e.g., landing pages for paid traffic and top pages for organic traffic — and then segment by New Users:

Be sure to conduct ecommerce data analysis like this for each channel your business invests in to better understand how effective each channel is at generating awareness and how users from that channel interact with your site.

You may find that users from a specific channel, like Pinterest, have a longer buying cycle because you’re engaging them so early in the purchase journey, whereas a new user through search may be looking for the specific products you sell and be ready to buy.

Conversion Analysis for Purchase Behavior

A conversion ecommerce data analysis focuses on the middle and bottom sections of your funnel. It’s used to get an idea of what behaviors — i.e., on-site actions like pages visited, searches, etc. — lead to a conversion:

By the end of this section and analysis, you should be able to answer four questions regarding purchasing behavior in your online funnel:

What behaviors lead to conversion?

How effectively are we encouraging these behaviors?

What segments of our user base are driving the highest conversions?

What channels drive the highest conversion?

Like we did above, you will also conduct a channel analysis but this time — instead of new users — you’ll segment all users or purchasers while also inspecting your revenue channels.

The main KPIs and metrics include:

Naturally, site-wide conversion numbers are a fantastic starting point. To do that, analyze your conversion metrics in the Shopify dashboard:

In the dashboard above, total store visits are down, but sales are up. How? The average order value increased by 9%, showing that while we aren’t getting tons of new middle-to-bottom funnel traction, our bottom funnel is still coming back for more.

These conversion metrics give you a baseline, but you can also run additional reports filtered by “traffic referrer” to see how sales and conversation rates vary by channel:

However, the best way to do this is by utilizing the custom Google Data Studio dashboards built for Shopify Plus merchants inside the full Data Analysis Course:

For multi-channel ecommerce, the next ecommerce data analysis is perhaps the most-valuable report this post contains.

Using the custom Google Data Studio dashboard within the course, you can measure (1) how new visitors compare to returning visitors as well as (2) how your sources and mediums (i.e., channels) perform:

Notice that returning visitors convert at a much higher percentage. This goes to show why brand loyalty is important and can help you build a case for investing more in remarketing.

Within these subsets, you can then analyze micro-conversions from each channel like add to cart, reached checkout, and more:

Once you’ve baselined your channels to see which produces the best conversion rates, it’s time to dig deeper and determine your Opportunity Pie and the reasoning behind those conversions.

The Opportunity Pie is the amount of traffic you get that can to convert. It helps to weed through hoards of traffic that will never convert, giving you a more realistic conversion analysis. This process is necessary because not everyone that clicks on your site can convert. Some might not need your product. Others might just be there for the secondary information.

One of the easiest ways to determine your Opportunity Pie is to exclude traffic from your site, that spent less than ten seconds on site. You can do this by creating a custom segment in Google Analytics:

This simple tweak to Analytics’ channel reports helps you weed through new visits who landed on your page but left immediately after recognizing that it wasn’t for them. Not removing them clouds your conversion data with potentially hundreds or thousands of visits a month that were never going to convert.

A second option is surveying web visitors to see what percentage are there to shop, browse or other reasons like applying for a job or a wholesale application. This exercise helps to sort the convertible visits from the unconvertable again.

To get a more detailed sense of user behavior, heat mapping and visitor recording technology to integrate directly with Shopify and see what triggers drive action on site.

Lastly, onsite search will open up a treasure trove of information — straight from your users — on exactly what they’re looking for:

From here, you can begin to analyze the user flows for each channel based on total conversions, seeing which pages and channels were critical drivers.

Loyalty Analysis for Brand Evangelists

It costs 5x the amount to get a new customer as it does to keep and upsell an existing one. The probability of selling to an existing customer is 60-70%, too. Meanwhile, selling to new users is extremely difficult.

The point is: your funnel doesn’t stop at conversions. Or at least, it shouldn’t. Creating loyal followings and turning customers into brand evangelists should always be of utmost priority.

Not only does it increase your lifetime value, allowing you to spend more on customer acquisition, it also enables free affiliate marketing from the words of actual customers.

Conducting a loyalty analysis should focus on answering five questions:

How effective are you at retaining customers?

How many of your customers are you ‘at risk’ of losing?

On average, how often do customers purchase?

What stops first-time purchasers from making a repeat purchase?

How can you improve your customers’ experience?

The main KPIs to track are customer lifetime value (LTV), repeat purchase rate, product reviews, and qualitative metrics like NPS, CSAT, and voice of the customer surveys.

Happiness and delight are difficult emotions to quantify. But with a combined approach, you can get a strong idea of how evangelical your buyers are.

Using this report can give you a clear picture of which channel produces the highest LTV customers. For example, in this sample report, organic search is generating tons of repeat customers at a high lifetime value, indicating that it’s succeeding in creating a flow from awareness to a loyal customer.

If you’re not sure whether your business is acquisition or repeat customer heavy, check the repeat customer rate report:

This report outlines if you are acquisition or retention focused. It also shows the balance between acquiring or keeping customers and where you can look to improve the loyalty-based portion of your funnel.

Another great metric to look at is your average time between purchases.

This will show you if you need to either:

Decrease the time in between purchases and cross-sell more often

Increase average order values by offering bundles or ways to increase the standard value of a product

If you have high purchase values or average order values, you need fewer purchases per year for each customer. Conversely, if you have a low average order value, you’ll need people to purchase more often to maintain a high LTV.

With products averaging around $6, it’d take countless new users to reach huge sales if a customer had small order values or didn’t purchase frequently. To combat this, the company instituted the Pura Vida Monthly Club, helping to increase the frequency of purchases by selling three bracelets via a monthly subscription for $14.95:

To help drive average order values, they also instituted free shipping when customers spend $25 or more:

Once you’ve analyzed these reports and have a solid foundation of your repeat customer rate, you should have answers to a few fundamental questions:

How effective are you at retaining customers?

On average, how often do they purchase?

If your repeat purchase rate is high, you’re already doing a fantastic job at creating loyal customers. If it could be higher, you have a great opportunity to increase it. Tools like the Net Promoter Score (NPS) will let you gauge customers satisfaction:

Using Customer Guru with Shopify, you can quickly survey customers at the end of a purchase and compare that back with average NPS’ for your industry.

Customer Guru surveys generate a 10x higher response rate than standard surveys due to their simplicity. They allow you to collect a simple feedback number on user satisfaction that indicates whether or not they will promote or detract from your business due to their experience.

Take this analysis one step further by segmenting your results based on customer behavior:

One-time buyers

Multiple purchases

Finish off your analysis with a qualitative, voice of customer survey.

The voice of the customer is an open-ended question that your customers or visitors can answer in their own voice. As Avinash Kaushik says in his book Web Analytics: 2.0,

It cannot, no matter how much you torture the data, tell you why something happened.

Only your visitors and customers can tell you why something happened, so engage them in a conversation through reviews and surveys.

Reporting Frequencies for Optimal Performance

Below are some general recommendations for how often to review reports and what main points on which to focus your time.

Take a deep look at each stage of the funnel from awareness to conversion to loyalty. Focus on running each analysis for each channel to see what can be improved.

Weekly

Complete a channel analysis for your main channels. If your focus is generating awareness, for example, you will need to review your advertising KPIs (as mentioned earlier in this post) to tweak campaigns and double down on the most successful ones.

Ready to grow your business with ecommerce data analysis?

Despite its size and depth, this article is an abbreviated excerpt from the Shopify Plus Data Analysis Course, one of many courses available exclusively to Shopify Plus merchants …

Let the Data Guide You

Conversion funnels are a law of nature. Just as gravity explains how water falls down a mountain, a funnel is our way of explaining why and how visitors interact with our website. Data analysis is just our way of tracking and explaining it.

Users build awareness over time and steadily travel down your funnel from conversion to brand evangelist.

But it’s not always that simple. Understanding which factors, channels, and tactics worked requires a deep analysis at each stage of the funnel.

To better improve both the user experience and your bottom line, stay on top of your online funnel, keep a repository of your analysis and constantly refine your ecommerce data analysis.